AI-Powered Football Player Performance Analysis
In the highly competitive and high-budget game of American Football, smooth and effective recruiting processes are crucial for a team’s professional and commercial success. Additionally, individual athlete performance has always been a cornerstone of a team's achievements.
Video capturing has become one of the most effective tools to document specific plays, and to store athletes’ highlights. This type of media also allows for the analysis and comparison of historical data. However, the video analysis process can be complex for the average person, as it requires exceptional analytical skills to extract the necessary performance data. Our client was in search of a solution to streamline their analytical workflow and to provide recruiters and coaches with the most precise data.
Challenges
Our team's challenge was to develop a custom AI solution capable of automatically tracking a player’s maximum speed and other athletic metrics within a video. The AI solution and its corresponding tool needed to be:
Simple to use
Robust enough to handle videos of various quality
Fast and precise.
Solution
We have created a web app with custom AI-powered algorithms that allow the user to select a specific player on the video and calculate the selected athleticism metric for the player. The custom AI solution uses computer vision algorithms to:
Detect and track specific players in each frame of the video
Detect the elements (yard lines, sidelines, hash marks, etc) of the playing field
Calculate:
Max Speed;
Time to Max Speed;
Yards Of Separation and Closing Time;
Yards after Catch;
Automatically edit the video to add tracking visualization
Detect and track specific players in each frame of the video
Detect the elements (yard lines, sidelines, hash marks, etc) of the playing field
Calculate:
Max Speed;
Time to Max Speed;
Yards Of Separation and Closing Time;
Yards after Catch;
Automatically edit the video to add tracking visualization
Features
Our solution has several features
Field recognition
Our AI solution can recognize field marking to determine the position of the players on the field
Trajectory reconstruction
The system tracks a specific player within each frame of the video and reconstructs the players’ trajectory on the field over time. This allows calculation of players’ speed and passing distance as well as the distance between players in some game moments
Player tracking
The user chooses the specific player they would like to track on the video frame. The system tracks this exact player within the video on each frame
Video processing
The system adds graphic elements to the original video to highlight the necessary player and metric values. The output is a video with this exact player graphically highlighted on the screen with their respective speed or other characteristic shown on each frame.
Tech Stack
Python
PyTorch
OpenCV
Results
As a result, we developed a solution that has become an invaluable asset for athlete recruitment and player allocation. The measurement tool became the backbone of the client’s success, allowing their analyst team to help athletes, parents, and coaches swiftly assess players' performance.
From a technical standpoint, the solution maintains an error rate of less than 2%, even when analyzing amateur videos. This high level of accuracy underscores the robustness and stability of the project
Additionally, the data provided by the solution has become essential for various recruitment businesses, helping recruiters to make strategic decisions with ease, using data provided by our client. The significant time saved on each performance analysis request has allowed the client to scale their business and pursue new strategic goals.
Additionally, the data provided by the solution has become essential for various recruitment businesses, helping recruiters to make strategic decisions with ease, using data provided by our client. The significant time saved on each performance analysis request has allowed the client to scale their business and pursue new strategic goals.
